{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"HF_ENDPOINT\"] = \"https://hf-mirror.com\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-3jDgur4Psai",
    "outputId": "49f8f2f6-4a92-41a7-b8b5-416c21015512"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datasets in /usr/local/lib/python3.10/site-packages (3.0.0)\n",
      "Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/site-packages (4.44.2)\n",
      "Requirement already satisfied: evaluate in /usr/local/lib/python3.10/site-packages (0.4.3)\n",
      "Requirement already satisfied: timm in /usr/local/lib/python3.10/site-packages (1.0.9)\n",
      "Requirement already satisfied: albumentations in /usr/local/lib/python3.10/site-packages (1.4.15)\n",
      "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/site-packages (0.34.2)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/site-packages (from datasets) (3.12.3)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/site-packages (from datasets) (1.25.2)\n",
      "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/site-packages (from datasets) (17.0.0)\n",
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      "Requirement already satisfied: pandas in /usr/local/lib/python3.10/site-packages (from datasets) (2.1.3)\n",
      "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/site-packages (from datasets) (2.32.3)\n",
      "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/site-packages (from datasets) (4.66.5)\n",
      "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/site-packages (from datasets) (3.5.0)\n",
      "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/site-packages (from datasets) (0.70.16)\n",
      "Requirement already satisfied: fsspec[http]<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/site-packages (from datasets) (2023.10.0)\n",
      "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/site-packages (from datasets) (3.10.5)\n",
      "Requirement already satisfied: huggingface-hub>=0.22.0 in /usr/local/lib/python3.10/site-packages (from datasets) (0.25.0)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.10/site-packages (from datasets) (23.2)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/site-packages (from datasets) (6.0.1)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/site-packages (from transformers[torch]) (2023.10.3)\n",
      "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/site-packages (from transformers[torch]) (0.4.5)\n",
      "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/site-packages (from transformers[torch]) (0.19.1)\n",
      "Requirement already satisfied: torch in /usr/local/lib/python3.10/site-packages (from transformers[torch]) (2.0.1+cu118)\n",
      "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/site-packages (from timm) (0.15.2+cu118)\n",
      "Requirement already satisfied: scipy>=1.10.0 in /usr/local/lib/python3.10/site-packages (from albumentations) (1.14.1)\n",
      "Requirement already satisfied: scikit-image>=0.21.0 in /usr/local/lib/python3.10/site-packages (from albumentations) (0.24.0)\n",
      "Requirement already satisfied: pydantic>=2.7.0 in /usr/local/lib/python3.10/site-packages (from albumentations) (2.9.2)\n",
      "Requirement already satisfied: albucore>=0.0.15 in /usr/local/lib/python3.10/site-packages (from albumentations) (0.0.16)\n",
      "Requirement already satisfied: eval-type-backport in /usr/local/lib/python3.10/site-packages (from albumentations) (0.2.0)\n",
      "Requirement already satisfied: opencv-python-headless>=4.9.0.80 in /usr/local/lib/python3.10/site-packages (from albumentations) (4.10.0.84)\n",
      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/site-packages (from accelerate) (5.9.5)\n",
      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/site-packages (from aiohttp->datasets) (2.4.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/site-packages (from aiohttp->datasets) (1.3.1)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/site-packages (from aiohttp->datasets) (23.1.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/site-packages (from aiohttp->datasets) (1.4.1)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/site-packages (from aiohttp->datasets) (6.1.0)\n",
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      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/site-packages (from huggingface-hub>=0.22.0->datasets) (4.8.0)\n",
      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/site-packages (from pydantic>=2.7.0->albumentations) (0.6.0)\n",
      "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/site-packages (from pydantic>=2.7.0->albumentations) (2.23.4)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/site-packages (from requests>=2.32.2->datasets) (3.2.0)\n",
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      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/site-packages (from requests>=2.32.2->datasets) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/site-packages (from requests>=2.32.2->datasets) (2023.7.22)\n",
      "Requirement already satisfied: networkx>=2.8 in /usr/local/lib/python3.10/site-packages (from scikit-image>=0.21.0->albumentations) (3.1)\n",
      "Requirement already satisfied: pillow>=9.1 in /usr/local/lib/python3.10/site-packages (from scikit-image>=0.21.0->albumentations) (10.0.0)\n",
      "Requirement already satisfied: imageio>=2.33 in /usr/local/lib/python3.10/site-packages (from scikit-image>=0.21.0->albumentations) (2.35.1)\n",
      "Requirement already satisfied: tifffile>=2022.8.12 in /usr/local/lib/python3.10/site-packages (from scikit-image>=0.21.0->albumentations) (2024.8.30)\n",
      "Requirement already satisfied: lazy-loader>=0.4 in /usr/local/lib/python3.10/site-packages (from scikit-image>=0.21.0->albumentations) (0.4)\n",
      "Requirement already satisfied: sympy in /usr/local/lib/python3.10/site-packages (from torch->transformers[torch]) (1.12)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/site-packages (from torch->transformers[torch]) (3.1.2)\n",
      "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/site-packages (from torch->transformers[torch]) (2.0.0)\n",
      "Requirement already satisfied: cmake in /usr/local/lib/python3.10/site-packages (from triton==2.0.0->torch->transformers[torch]) (3.27.4.1)\n",
      "Requirement already satisfied: lit in /usr/local/lib/python3.10/site-packages (from triton==2.0.0->torch->transformers[torch]) (16.0.6)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/site-packages (from pandas->datasets) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/site-packages (from pandas->datasets) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/site-packages (from pandas->datasets) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/site-packages (from jinja2->torch->transformers[torch]) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/site-packages (from sympy->torch->transformers[torch]) (1.3.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -U datasets transformers[torch] evaluate timm albumentations accelerate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "w3IRGyjVPo3Y"
   },
   "outputs": [],
   "source": [
    "# https://huggingface.co/learn/computer-vision-course/unit3/vision-transformers/vision-transformer-for-objection-detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "0f6HdQSvPo3a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import albumentations\n",
    "import numpy as np\n",
    "from datasets import load_dataset\n",
    "from transformers import (\n",
    "    AutoImageProcessor,\n",
    "    AutoModelForObjectDetection,\n",
    "    Trainer,\n",
    "    TrainingArguments,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299,
     "referenced_widgets": [
      "3c20cfe6c19647258e88b668628e3be9",
      "bcc481f270f8436384dcde27386c5455",
      "6ce50c517e02458695ff13c70666dcf1",
      "68f7145f8ca34c6eb6191a85bae108f5",
      "041312a4dcc74fa18df0adc2a6c6ee2a",
      "004a7355bfcd446c8549f5fd062bfb8a",
      "30ce93e3e4084395a8bb729345cf26ff",
      "c0790d672cf8415d9ae5be3aca9b0127",
      "693b9162334340b2a23728332a4c0ae8",
      "efe801e5db774fb482efe00b723178fa",
      "bab7fe6f15f947cca521de097fdfad07",
      "979f3abfd1594e9caba95138b46f701e",
      "69d3414bf3564c4495f172104d0dd3f5",
      "443ab5ed3ffb418082d3fb3e14af70b0",
      "0adc1c1054194c329116e7747927982c",
      "b1f1276cf2e248c5b8f7fc70d0e06e35",
      "d54b5794ede64b5c865abfe2383be33e",
      "1c9f3243977942359a098fbe7af180f2",
      "4296510053d94940982c9d733b98556a",
      "5065ac052a274181bcf70358fb5dad25",
      "5b7319a1711a4781a11f383b4dfc55fe",
      "06d1f2000e614250986099123185767e",
      "21ae6ff451d9447bb6cbf712b96af194",
      "508583003dc645babd768abca7a11513",
      "42244b56c7ce47e1bccceefd7588987b",
      "c3b07f664b8647fb93ef89f7c9baf8db",
      "56a4371d5e2f4733945f3e894226e66b",
      "d90ac8d22205466abb5d9e802674f84b",
      "6aa232f8ab2d49a193d88601d19c03e7",
      "12a3fad7b01f40219e5ddbf1bc506e77",
      "5a160238abf749e285285f5dee7c862f",
      "c6e2e06147dc4feda9f789d78061d596",
      "8bdb392bd271485089ab50e3fbd2cb1d",
      "13bfa55e58e94fc28f2faad1793a76cc",
      "1f17118c18294752be871e3a3b6e9f29",
      "9637bac2730e46dfb43b1b89a9a23ad3",
      "0eaad4e58c024cfaa342fb34a3f6242f",
      "193a7656b22a44d18b788edc224e8756",
      "b0e45495d6604276b7b6a41923725029",
      "6dd9a18d09a14fadbe60d524730a9185",
      "c6493cbcf53d45358646dfd3229c127f",
      "ab02f364bffa4fc4a4f64e693aee7ef3",
      "4e955ced7969462db5cb487e9f76a362",
      "4018c22df2864fc1ba76089fc125fd07",
      "8440bcac034b46ea8ef4ec9e09e0b561",
      "8e5235fa138245b48194b0701cb1f76a",
      "4792ebb8fbe94a349c22ff10b2199393",
      "25175f6b2a3d43a7832eec3f59350372",
      "52ed46867c654af995b70c71163b800d",
      "943a4779ea104ce09a4e6a574f0d1e44",
      "13ea83af0fcd483ab6585f8b30b75964",
      "57e3a1ec44b746d1aec89d9e89e8b1ca",
      "edf5e4f74a4f42c787887023a7c023e8",
      "5d50c9bb86fe4dc6b1e4b7d88f1a9561",
      "d588dbe0a54d4330ad7feb314259a5d2"
     ]
    },
    "id": "BAImFHaUPo3a",
    "outputId": "3e7ce997-26f4-47e8-bd48-bd219ba02bfd"
   },
   "outputs": [],
   "source": [
    "data = load_dataset(\"moyanxinxu/container\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f43fCAsFPo3b",
    "outputId": "5f60f365-95a9-471a-8976-34441ae48fa9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'image_id', 'width', 'height', 'objects'],\n",
       "        num_rows: 1049\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['image', 'image_id', 'width', 'height', 'objects'],\n",
       "        num_rows: 263\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "5f28ae746ee34385ad457af0c556b50c",
      "76401d58dcb142c9a6e5ea363ae0eeb8",
      "7b7dfcc853f740d8a5e44f69373049c5",
      "f0205ec9d56f41538dbc8876ef5d423a",
      "5933c0f304694621968af1024b67df72",
      "8c45fb72b62f4e5ea6d798f3e3f146c7",
      "16418df283074d06b2b47c3724e70509",
      "f7205f84954044ce978c9b7ac35a84b5",
      "d5dfde0dacd944d6bb07fd712ccaaacc",
      "4f592d3e566141adbc4d43095e25f613",
      "9b76012878ea4d1b9e83d9ddaf437866"
     ]
    },
    "id": "Rk5Qp11mPo3c",
    "outputId": "21e05d3d-e4f1-4f3f-de6a-0505ad52e50d"
   },
   "outputs": [],
   "source": [
    "preprocessor = AutoImageProcessor.from_pretrained(\"facebook/detr-resnet-50\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MaAlsdvhPo3c",
    "outputId": "afa64005-ca91-4e88-bbb3-a6f1382dfe02"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1120x1080>,\n",
       " 'image_id': 475,\n",
       " 'width': 1120,\n",
       " 'height': 1080,\n",
       " 'objects': {'area': [23562.0],\n",
       "  'bbox': [[415.0, 592.0, 374.0, 63.0]],\n",
       "  'category': ['container'],\n",
       "  'id': [0]}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"train\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "TGeureEKPo3c"
   },
   "outputs": [],
   "source": [
    "aug = albumentations.Compose(\n",
    "    transforms=[\n",
    "        albumentations.Resize(480, 480),\n",
    "        albumentations.HorizontalFlip(p=1),\n",
    "        albumentations.RandomBrightnessContrast(p=1.0),\n",
    "    ],\n",
    "    bbox_params=albumentations.BboxParams(format=\"coco\", label_fields=[\"category\"]),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "BlCmZgR-Po3d"
   },
   "outputs": [],
   "source": [
    "def datapipe(data):\n",
    "    images, bboxes, areas, categories, targets = [], [], [], [], []\n",
    "\n",
    "    image_ids = data[\"image_id\"]\n",
    "\n",
    "    for image, objects in zip(data[\"image\"], data[\"objects\"]):\n",
    "        image = np.array(image.convert(\"RGB\"))[:, :, ::-1]\n",
    "        out = aug(image=image, bboxes=objects[\"bbox\"], category=objects[\"id\"])\n",
    "\n",
    "        areas.append(objects[\"area\"])\n",
    "\n",
    "        images.append(out[\"image\"])\n",
    "        bboxes.append(out[\"bboxes\"])\n",
    "        categories.append(out[\"category\"])\n",
    "\n",
    "    for image_id, category, area, box in zip(image_ids, categories, areas, bboxes):\n",
    "        annotations = []\n",
    "\n",
    "        for _category, _area, _box in zip(category, area, box):\n",
    "            new_ann = {\n",
    "                \"image_id\": image_id,\n",
    "                \"category_id\": _category,\n",
    "                \"isCrowd\": 0,\n",
    "                \"area\": _area,\n",
    "                \"bbox\": list(_box),\n",
    "            }\n",
    "            annotations.append(new_ann)\n",
    "        targets.append({\"image_id\": image_id, \"annotations\": annotations})\n",
    "    return preprocessor(images=images, annotations=targets, return_tensors=\"pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Elhp6vsRPo3d"
   },
   "outputs": [],
   "source": [
    "train_data = data[\"train\"].with_transform(datapipe)\n",
    "# val_data = data[\"validation\"].with_transform(datapipe)\n",
    "test_data = data[\"test\"].with_transform(datapipe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "PkcmrVQ1Po3d"
   },
   "outputs": [],
   "source": [
    "def collate_fn(batch):\n",
    "    pixel_values = [item[\"pixel_values\"] for item in batch]\n",
    "    output = preprocessor.pad(pixel_values, return_tensors=\"pt\")\n",
    "    labels = [item[\"labels\"] for item in batch]\n",
    "\n",
    "    ret = {}\n",
    "\n",
    "    ret[\"pixel_values\"] = output[\"pixel_values\"]\n",
    "    ret[\"pixel_mask\"] = output[\"pixel_mask\"]\n",
    "    ret[\"labels\"] = labels\n",
    "\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_9li6x0bPo3d",
    "outputId": "61ce4a26-7f73-45b8-d0e1-c9de5cdae83e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The `max_size` parameter is deprecated and will be removed in v4.26. Please specify in `size['longest_edge'] instead`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pixel_values': tensor([[[-0.3198, -0.3712, -0.4568,  ...,  0.0569,  0.0398,  0.0398],\n",
      "         [-0.3883, -0.4397, -0.5424,  ...,  0.0569,  0.0398,  0.0398],\n",
      "         [-0.4739, -0.5424, -0.6623,  ...,  0.0398,  0.0227,  0.0227],\n",
      "         ...,\n",
      "         [ 0.3138,  0.2796,  0.2282,  ...,  0.2282,  0.2624,  0.2796],\n",
      "         [ 0.2453,  0.2453,  0.2624,  ...,  0.2624,  0.2796,  0.2624],\n",
      "         [ 0.1939,  0.2282,  0.2967,  ...,  0.2967,  0.2796,  0.2624]],\n",
      "\n",
      "        [[-0.1275, -0.2150, -0.3375,  ...,  0.1001,  0.0826,  0.0826],\n",
      "         [-0.1975, -0.3025, -0.4251,  ...,  0.1001,  0.0826,  0.0826],\n",
      "         [-0.3200, -0.4251, -0.5651,  ...,  0.0826,  0.0651,  0.0651],\n",
      "         ...,\n",
      "         [ 0.5028,  0.4678,  0.4328,  ...,  0.1527,  0.2052,  0.2402],\n",
      "         [ 0.4328,  0.4328,  0.4678,  ...,  0.1702,  0.1702,  0.1702],\n",
      "         [ 0.3803,  0.4153,  0.4853,  ...,  0.1702,  0.1527,  0.1352]],\n",
      "\n",
      "        [[ 0.0953,  0.0082, -0.1138,  ...,  0.2348,  0.2173,  0.1999],\n",
      "         [ 0.0082, -0.0964, -0.2184,  ...,  0.2348,  0.2173,  0.1999],\n",
      "         [-0.1312, -0.2358, -0.3753,  ...,  0.2173,  0.1999,  0.1825],\n",
      "         ...,\n",
      "         [ 0.7925,  0.7576,  0.7054,  ...,  0.3568,  0.4265,  0.4788],\n",
      "         [ 0.7054,  0.7228,  0.7402,  ...,  0.3393,  0.3916,  0.4091],\n",
      "         [ 0.6531,  0.7054,  0.7751,  ...,  0.3393,  0.3568,  0.3568]]]), 'pixel_mask': tensor([[1, 1, 1,  ..., 1, 1, 1],\n",
      "        [1, 1, 1,  ..., 1, 1, 1],\n",
      "        [1, 1, 1,  ..., 1, 1, 1],\n",
      "        ...,\n",
      "        [1, 1, 1,  ..., 1, 1, 1],\n",
      "        [1, 1, 1,  ..., 1, 1, 1],\n",
      "        [1, 1, 1,  ..., 1, 1, 1]]), 'labels': {'size': tensor([800, 800]), 'image_id': tensor([475]), 'class_labels': tensor([0]), 'boxes': tensor([[0.4625, 0.5773, 0.3339, 0.0583]]), 'area': tensor([65449.9961]), 'iscrowd': tensor([0]), 'orig_size': tensor([480, 480])}}\n"
     ]
    }
   ],
   "source": [
    "print(train_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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    },
    "id": "DdoRYzw3Po3e",
    "outputId": "c3f5f902-4f3b-49c1-a9f4-8bbf7945c277"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at facebook/detr-resnet-50 were not used when initializing DetrForObjectDetection: ['model.backbone.conv_encoder.model.layer1.0.downsample.1.num_batches_tracked', 'model.backbone.conv_encoder.model.layer2.0.downsample.1.num_batches_tracked', 'model.backbone.conv_encoder.model.layer3.0.downsample.1.num_batches_tracked', 'model.backbone.conv_encoder.model.layer4.0.downsample.1.num_batches_tracked']\n",
      "- This IS expected if you are initializing DetrForObjectDetection from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DetrForObjectDetection from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DetrForObjectDetection were not initialized from the model checkpoint at facebook/detr-resnet-50 and are newly initialized because the shapes did not match:\n",
      "- class_labels_classifier.bias: found shape torch.Size([92]) in the checkpoint and torch.Size([2]) in the model instantiated\n",
      "- class_labels_classifier.weight: found shape torch.Size([92, 256]) in the checkpoint and torch.Size([2, 256]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "id2label = {0: \"container\"}\n",
    "label2id = {\"container\": 0}\n",
    "\n",
    "\n",
    "model = AutoModelForObjectDetection.from_pretrained(\n",
    "    \"facebook/detr-resnet-50\",\n",
    "    id2label=id2label,\n",
    "    label2id=label2id,\n",
    "    ignore_mismatched_sizes=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "H3ot8peaPo3e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Detected kernel version 4.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    }
   ],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir=\"detr-resnet-50-container-finetuned\",\n",
    "    per_device_train_batch_size=10,\n",
    "    num_train_epochs=20,\n",
    "    eval_strategy=\"epoch\",\n",
    "    learning_rate=1e-5,\n",
    "    weight_decay=1e-4,\n",
    "    save_total_limit=2,\n",
    "    remove_unused_columns=False,\n",
    ")\n",
    "\n",
    "# Define the trainer\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    data_collator=collate_fn,\n",
    "    train_dataset=train_data,\n",
    "    eval_dataset=test_data,\n",
    "    tokenizer=preprocessor,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "id": "hHi5a17hPo3e",
    "outputId": "9cc107ed-e889-4b48-8da0-79606371c96b"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1727' max='2100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1727/2100 1:12:43 < 15:43, 0.40 it/s, Epoch 16.44/20]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>2.409549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>No log</td>\n",
       "      <td>1.270554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>No log</td>\n",
       "      <td>1.100660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>No log</td>\n",
       "      <td>1.064438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.888600</td>\n",
       "      <td>0.942410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.888600</td>\n",
       "      <td>0.879503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.888600</td>\n",
       "      <td>0.828298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.888600</td>\n",
       "      <td>0.816551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.888600</td>\n",
       "      <td>0.780028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.844300</td>\n",
       "      <td>0.736942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.844300</td>\n",
       "      <td>0.753279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.844300</td>\n",
       "      <td>0.741595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.844300</td>\n",
       "      <td>0.691074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.844300</td>\n",
       "      <td>0.692918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.733900</td>\n",
       "      <td>0.700351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.733900</td>\n",
       "      <td>0.674958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    },
    "id": "XF_hv81YeHqq",
    "outputId": "841cbd91-356d-483d-95f2-cdc6b4019295"
   },
   "outputs": [],
   "source": [
    "!tar -zcvf model.tar.gz detr-resnet-50-container-finetuned/"
   ]
  }
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